How to filter numpy array by list of indices?
Question:
I have a numpy array, filtered__rows, comprised of LAS data [x, y, z, intensity, classification]. I have created a cKDTree of points and have found nearest neighbors, query_ball_point, which is a list of indices for the point and its neighbors.
Is there a way to filter filtered__rows to create an array of only points whose index is in the list returned by query_ball_point?
Answers:
It looks like you just need a basic integer array indexing:
filter_indices = [1,3,5]
np.array([11,13,155,22,0xff,32,56,88])[filter_indices]
Do you know how that translates for multi-dimensional arrays?
It can be expanded to multi dimensional arrays by giving a 1d array for every index so for a 2d array
filter_indices=np.array([[1,0],[0,1]])
array=np.array([[0,1],[1,2]])
print(array[filter_indices[:,0],filter_indices[:,1]])
will give you :
[1,1]
Scipy has an explanation on what will happen if you call:
print(array[filter_indices])
Docs – https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
numpy.take can be useful and works well for multimensional arrays.
import numpy as np
filter_indices = [1, 2]
array = np.array([[1, 2, 3, 4, 5],
[10, 20, 30, 40, 50],
[100, 200, 300, 400, 500]])
axis = 0
print(np.take(array, filter_indices, axis))
# [[ 10 20 30 40 50]
# [100 200 300 400 500]]
axis = 1
print(np.take(array, filter_indices, axis))
# [[ 2 3]
# [ 20 30]
# [200 300]]
Using Docs: https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
The following implementation should work for arbitrary number of dimensions/shapes for some numpy ndarray.
First we need a multi-dimensional set of indexes and some example data:
import numpy as np
y = np.arange(35).reshape(5,7)
print(y)
indexlist = [[0,1], [0,2], [3,3]]
print ('indexlist:', indexlist)
To actually extract the intuitive result the trick is to use a Transpose:
indexlisttranspose = np.array(indexlist).T.tolist()
print ('indexlist.T:', indexlisttranspose)
print ('y[indexlist.T]:', y[ tuple(indexlisttranspose) ])
Makes the following terminal output:
y: [[ 0 1 2 3 4 5 6]
[ 7 8 9 10 11 12 13]
[14 15 16 17 18 19 20]
[21 22 23 24 25 26 27]
[28 29 30 31 32 33 34]]
indexlist: [[0, 1], [0, 2], [3, 3]]
indexlist.T: [[0, 0, 3], [1, 2, 3]]
y[indexlist.T]: [ 1 2 24]
The tuple… fixes a future warning which we can cause like so:
print ('y[indexlist.T]:', y[ indexlisttranspose ])
FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`.
In the future this will be interpreted as an array index,
`arr[np.array(seq)]`, which will result either in an error or a
different result.
print ('y[indexlist.T]:', y[ indexlisttranspose ])
y[indexlist.T]: [ 1 2 24]
The fastest way to do this is X[tuple(index.T)], where X is the ndarray with the elements and index is the ndarray of indices wished to be retrieved.
I have a numpy array, filtered__rows, comprised of LAS data [x, y, z, intensity, classification]. I have created a cKDTree of points and have found nearest neighbors, query_ball_point, which is a list of indices for the point and its neighbors.
Is there a way to filter filtered__rows to create an array of only points whose index is in the list returned by query_ball_point?
It looks like you just need a basic integer array indexing:
filter_indices = [1,3,5]
np.array([11,13,155,22,0xff,32,56,88])[filter_indices]
Do you know how that translates for multi-dimensional arrays?
It can be expanded to multi dimensional arrays by giving a 1d array for every index so for a 2d array
filter_indices=np.array([[1,0],[0,1]])
array=np.array([[0,1],[1,2]])
print(array[filter_indices[:,0],filter_indices[:,1]])
will give you :
[1,1]
Scipy has an explanation on what will happen if you call:
print(array[filter_indices])
Docs – https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
numpy.take can be useful and works well for multimensional arrays.
import numpy as np
filter_indices = [1, 2]
array = np.array([[1, 2, 3, 4, 5],
[10, 20, 30, 40, 50],
[100, 200, 300, 400, 500]])
axis = 0
print(np.take(array, filter_indices, axis))
# [[ 10 20 30 40 50]
# [100 200 300 400 500]]
axis = 1
print(np.take(array, filter_indices, axis))
# [[ 2 3]
# [ 20 30]
# [200 300]]
Using Docs: https://docs.scipy.org/doc/numpy-1.13.0/user/basics.indexing.html
The following implementation should work for arbitrary number of dimensions/shapes for some numpy ndarray.
First we need a multi-dimensional set of indexes and some example data:
import numpy as np
y = np.arange(35).reshape(5,7)
print(y)
indexlist = [[0,1], [0,2], [3,3]]
print ('indexlist:', indexlist)
To actually extract the intuitive result the trick is to use a Transpose:
indexlisttranspose = np.array(indexlist).T.tolist()
print ('indexlist.T:', indexlisttranspose)
print ('y[indexlist.T]:', y[ tuple(indexlisttranspose) ])
Makes the following terminal output:
y: [[ 0 1 2 3 4 5 6] [ 7 8 9 10 11 12 13] [14 15 16 17 18 19 20] [21 22 23 24 25 26 27] [28 29 30 31 32 33 34]] indexlist: [[0, 1], [0, 2], [3, 3]] indexlist.T: [[0, 0, 3], [1, 2, 3]] y[indexlist.T]: [ 1 2 24]
The tuple… fixes a future warning which we can cause like so:
print ('y[indexlist.T]:', y[ indexlisttranspose ])
FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. print ('y[indexlist.T]:', y[ indexlisttranspose ]) y[indexlist.T]: [ 1 2 24]
The fastest way to do this is X[tuple(index.T)], where X is the ndarray with the elements and index is the ndarray of indices wished to be retrieved.